Author: Raghav Tiwari, Sneha Kulkarni, Aniket Mishra, Meera Joshi
Abstract: Battery life remains one of the most critical factors influencing user satisfaction in mobile devices. With the increasing complexity of mobile applications, continuous background processes, and hardware-intensive features such as AI inference, location tracking, and real-time analytics, power consumption has become a major design challenge. Apple’s iOS platform has continuously evolved its battery optimization strategies by combining system-level controls with artificial intelligence driven power management techniques. Recent versions of iOS integrate machine learning models to predict user behavior, optimize background activity, and dynamically manage CPU, GPU, and network resources. This paper presents a comprehensive review of battery optimization techniques in iOS, with a special focus on AI-driven power management mechanisms. It discusses traditional energy-saving approaches, system architecture, developer-level APIs, and the role of on-device intelligence in adaptive energy control. Furthermore, this paper compares conventional rule-based power optimization with AI-assisted strategies and analyzes their impact on application performance and user experience. Challenges, limitations, and future research directions are also highlighted. The study aims to provide useful insights for iOS developers and researchers working on energy-efficient mobile computing.
Keywords: Battery optimization, iOS power management, AI-driven energy control, mobile energy efficiency, machine learning, iOS performance tuning
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